Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations10324
Missing cells12317
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory214.0 B

Variable types

Numeric18
Categorical5
DateTime1
Text3
Boolean5

Alerts

brand is highly imbalanced (62.3%)Imbalance
shipment_mode_Air Charter is highly imbalanced (66.1%)Imbalance
shipment_mode_Ocean is highly imbalanced (77.7%)Imbalance
shipment_mode_missing is highly imbalanced (78.2%)Imbalance
weight_kilograms has 3952 (38.3%) missing valuesMissing
freight_cost_usd has 4126 (40.0%) missing valuesMissing
line_item_insurance_usd has 287 (2.8%) missing valuesMissing
weight_category has 3952 (38.3%) missing valuesMissing
unit_price is highly skewed (γ1 = 40.58484939)Skewed
weight_kilograms is highly skewed (γ1 = 42.50590318)Skewed
id has unique valuesUnique

Reproduction

Analysis started2024-08-28 14:56:53.023477
Analysis finished2024-08-28 14:58:43.309954
Duration1 minute and 50.29 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct10324
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51098.968
Minimum1
Maximum86823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:43.533066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5151.2
Q112795.75
median57540.5
Q383648.25
95-th percentile86167.85
Maximum86823
Range86822
Interquartile range (IQR)70852.5

Descriptive statistics

Standard deviation31944.332
Coefficient of variation (CV)0.62514633
Kurtosis-1.6398372
Mean51098.968
Median Absolute Deviation (MAD)27404
Skewness-0.23036671
Sum5.2754575 × 108
Variance1.0204404 × 109
MonotonicityNot monotonic
2024-08-28T14:58:43.833869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
82565 1
 
< 0.1%
82594 1
 
< 0.1%
82595 1
 
< 0.1%
82596 1
 
< 0.1%
82597 1
 
< 0.1%
82599 1
 
< 0.1%
82600 1
 
< 0.1%
82601 1
 
< 0.1%
82602 1
 
< 0.1%
Other values (10314) 10314
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
23 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
ValueCountFrequency (%)
86823 1
< 0.1%
86822 1
< 0.1%
86821 1
< 0.1%
86819 1
< 0.1%
86818 1
< 0.1%
86817 1
< 0.1%
86816 1
< 0.1%
86815 1
< 0.1%
86814 1
< 0.1%
86813 1
< 0.1%

country
Categorical

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
South Africa
1406 
Nigeria
1194 
Côte d'Ivoire
1083 
Uganda
779 
Vietnam
688 
Other values (38)
5174 

Length

Max length18
Median length12
Mean length8.4762689
Min length4

Characters and Unicode

Total characters87509
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCôte d'Ivoire
2nd rowVietnam
3rd rowCôte d'Ivoire
4th rowVietnam
5th rowVietnam

Common Values

ValueCountFrequency (%)
South Africa 1406
13.6%
Nigeria 1194
11.6%
Côte d'Ivoire 1083
10.5%
Uganda 779
 
7.5%
Vietnam 688
 
6.7%
Zambia 683
 
6.6%
Haiti 655
 
6.3%
Mozambique 631
 
6.1%
Zimbabwe 538
 
5.2%
Tanzania 519
 
5.0%
Other values (33) 2148
20.8%

Length

2024-08-28T14:58:44.124856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 1570
11.7%
africa 1406
 
10.5%
nigeria 1194
 
8.9%
côte 1083
 
8.1%
d'ivoire 1083
 
8.1%
uganda 779
 
5.8%
vietnam 688
 
5.1%
zambia 683
 
5.1%
haiti 655
 
4.9%
mozambique 631
 
4.7%
Other values (38) 3596
26.9%

Most occurring characters

ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

shipment_mode
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Air
6113 
Truck
2830 
Air Charter
650 
Ocean
 
371
missing
 
360

Length

Max length11
Median length3
Mean length4.2632701
Min length3

Characters and Unicode

Total characters44014
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAir
2nd rowAir
3rd rowAir
4th rowAir
5th rowAir

Common Values

ValueCountFrequency (%)
Air 6113
59.2%
Truck 2830
27.4%
Air Charter 650
 
6.3%
Ocean 371
 
3.6%
missing 360
 
3.5%

Length

2024-08-28T14:58:44.448210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T14:58:44.757286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
air 6763
61.6%
truck 2830
25.8%
charter 650
 
5.9%
ocean 371
 
3.4%
missing 360
 
3.3%

Most occurring characters

ValueCountFrequency (%)
r 10893
24.7%
i 7483
17.0%
A 6763
15.4%
c 3201
 
7.3%
T 2830
 
6.4%
u 2830
 
6.4%
k 2830
 
6.4%
e 1021
 
2.3%
a 1021
 
2.3%
n 731
 
1.7%
Other values (8) 4411
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10893
24.7%
i 7483
17.0%
A 6763
15.4%
c 3201
 
7.3%
T 2830
 
6.4%
u 2830
 
6.4%
k 2830
 
6.4%
e 1021
 
2.3%
a 1021
 
2.3%
n 731
 
1.7%
Other values (8) 4411
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10893
24.7%
i 7483
17.0%
A 6763
15.4%
c 3201
 
7.3%
T 2830
 
6.4%
u 2830
 
6.4%
k 2830
 
6.4%
e 1021
 
2.3%
a 1021
 
2.3%
n 731
 
1.7%
Other values (8) 4411
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10893
24.7%
i 7483
17.0%
A 6763
15.4%
c 3201
 
7.3%
T 2830
 
6.4%
u 2830
 
6.4%
k 2830
 
6.4%
e 1021
 
2.3%
a 1021
 
2.3%
n 731
 
1.7%
Other values (8) 4411
10.0%
Distinct2006
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Minimum2006-05-02 00:00:00
Maximum2015-12-31 00:00:00
2024-08-28T14:58:45.012898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:45.320081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Adult
6595 
Pediatric
1955 
HIV test
1567 
HIV test - Ancillary
 
161
Malaria
 
30

Length

Max length20
Median length5
Mean length6.4494382
Min length3

Characters and Unicode

Total characters66584
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIV test
2nd rowPediatric
3rd rowHIV test
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 6595
63.9%
Pediatric 1955
 
18.9%
HIV test 1567
 
15.2%
HIV test - Ancillary 161
 
1.6%
Malaria 30
 
0.3%
ACT 16
 
0.2%

Length

2024-08-28T14:58:45.636026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T14:58:45.938641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
adult 6595
53.3%
pediatric 1955
 
15.8%
hiv 1728
 
14.0%
test 1728
 
14.0%
161
 
1.3%
ancillary 161
 
1.3%
malaria 30
 
0.2%
act 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

vendor
Text

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:46.401976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length65
Median length13
Mean length18.53332
Min length7

Characters and Unicode

Total characters191338
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.2%

Sample

1st rowRANBAXY Fine Chemicals LTD.
2nd rowAurobindo Pharma Limited
3rd rowAbbott GmbH & Co. KG
4th rowSUN PHARMACEUTICAL INDUSTRIES LTD (RANBAXY LABORATORIES LIMITED)
5th rowAurobindo Pharma Limited
ValueCountFrequency (%)
scms 5404
16.5%
rdc 5404
16.5%
from 5404
16.5%
limited 1288
 
3.9%
ltd 1169
 
3.6%
orgenics 754
 
2.3%
s 717
 
2.2%
buys 715
 
2.2%
wholesaler 715
 
2.2%
laboratories 705
 
2.2%
Other values (158) 10470
32.0%
2024-08-28T14:58:47.228100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%
Distinct184
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:47.712884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters72268
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st rowSKU0000
2nd rowSKU0001
3rd rowSKU0002
4th rowSKU0003
5th rowSKU0004
ValueCountFrequency (%)
sku0026 755
 
7.3%
sku0007 623
 
6.0%
sku0011 597
 
5.8%
sku0054 580
 
5.6%
sku0002 577
 
5.6%
sku0003 378
 
3.7%
sku0009 369
 
3.6%
sku0014 317
 
3.1%
sku0107 301
 
2.9%
sku0058 287
 
2.8%
Other values (174) 5540
53.7%
2024-08-28T14:58:48.432093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21660
30.0%
S 10324
14.3%
K 10324
14.3%
U 10324
14.3%
1 5240
 
7.3%
2 2804
 
3.9%
4 2693
 
3.7%
5 1831
 
2.5%
6 1749
 
2.4%
3 1586
 
2.2%
Other values (3) 3733
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21660
30.0%
S 10324
14.3%
K 10324
14.3%
U 10324
14.3%
1 5240
 
7.3%
2 2804
 
3.9%
4 2693
 
3.7%
5 1831
 
2.5%
6 1749
 
2.4%
3 1586
 
2.2%
Other values (3) 3733
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21660
30.0%
S 10324
14.3%
K 10324
14.3%
U 10324
14.3%
1 5240
 
7.3%
2 2804
 
3.9%
4 2693
 
3.7%
5 1831
 
2.5%
6 1749
 
2.4%
3 1586
 
2.2%
Other values (3) 3733
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21660
30.0%
S 10324
14.3%
K 10324
14.3%
U 10324
14.3%
1 5240
 
7.3%
2 2804
 
3.9%
4 2693
 
3.7%
5 1831
 
2.5%
6 1749
 
2.4%
3 1586
 
2.2%
Other values (3) 3733
 
5.2%
Distinct184
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:48.823495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length113
Median length79
Mean length50.144808
Min length31

Characters and Unicode

Total characters517695
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st rowHIV, Reveal G3 Rapid HIV-1 Antibody Test, 30 Tests
2nd rowNevirapine 10mg/ml, oral suspension, Bottle, 240 ml
3rd rowHIV 1/2, Determine Complete HIV Kit, 100 Tests
4th rowLamivudine 150mg, tablets, 60 Tabs
5th rowStavudine 30mg, capsules, 60 Caps
ValueCountFrequency (%)
tablets 6733
 
10.3%
tabs 6711
 
10.2%
60 4269
 
6.5%
hiv 3006
 
4.6%
30 2598
 
4.0%
tests 1616
 
2.5%
kit 1579
 
2.4%
1/2 1524
 
2.3%
disoproxil 1300
 
2.0%
fumarate 1300
 
2.0%
Other values (289) 34956
53.3%
2024-08-28T14:58:49.532390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 517695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 517695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 517695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

brand
Categorical

IMBALANCE 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Generic
7285 
Determine
799 
Uni-Gold
 
373
Aluvia
 
250
Kaletra
 
165
Other values (43)
1452 

Length

Max length15
Median length7
Mean length7.2879698
Min length3

Characters and Unicode

Total characters75241
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowReveal
2nd rowGeneric
3rd rowDetermine
4th rowGeneric
5th rowGeneric

Common Values

ValueCountFrequency (%)
Generic 7285
70.6%
Determine 799
 
7.7%
Uni-Gold 373
 
3.6%
Aluvia 250
 
2.4%
Kaletra 165
 
1.6%
Norvir 136
 
1.3%
Stat-Pak 115
 
1.1%
Bioline 113
 
1.1%
Truvada 94
 
0.9%
Videx 84
 
0.8%
Other values (38) 910
 
8.8%

Length

2024-08-28T14:58:49.840202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
generic 7285
69.7%
determine 799
 
7.6%
uni-gold 373
 
3.6%
aluvia 250
 
2.4%
kaletra 165
 
1.6%
norvir 136
 
1.3%
videx 125
 
1.2%
stat-pak 115
 
1.1%
bioline 113
 
1.1%
truvada 94
 
0.9%
Other values (40) 995
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

line_item_quantity
Real number (ℝ)

Distinct5065
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18332.535
Minimum1
Maximum619999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:50.097920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1408
median3000
Q317039.75
95-th percentile90951.55
Maximum619999
Range619998
Interquartile range (IQR)16631.75

Descriptive statistics

Standard deviation40035.303
Coefficient of variation (CV)2.1838389
Kurtosis40.0503
Mean18332.535
Median Absolute Deviation (MAD)2950
Skewness5.0383147
Sum1.8926509 × 108
Variance1.6028255 × 109
MonotonicityNot monotonic
2024-08-28T14:58:51.305205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 93
 
0.9%
1000 91
 
0.9%
100 87
 
0.8%
2000 73
 
0.7%
5000 69
 
0.7%
500 67
 
0.6%
20000 67
 
0.6%
3000 66
 
0.6%
3 63
 
0.6%
50000 62
 
0.6%
Other values (5055) 9586
92.9%
ValueCountFrequency (%)
1 35
0.3%
2 40
0.4%
3 63
0.6%
4 46
0.4%
5 28
0.3%
6 48
0.5%
7 27
0.3%
8 26
0.3%
9 22
 
0.2%
10 54
0.5%
ValueCountFrequency (%)
619999 1
 
< 0.1%
600906 1
 
< 0.1%
555197 1
 
< 0.1%
515000 3
< 0.1%
514526 1
 
< 0.1%
460041 1
 
< 0.1%
440000 1
 
< 0.1%
438409 1
 
< 0.1%
401961 1
 
< 0.1%
400000 2
< 0.1%

line_item_value
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157650.57
Minimum0
Maximum5951990.4
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:51.605818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192.5755
Q14314.5925
median30471.465
Q3166447.14
95-th percentile702831
Maximum5951990.4
Range5951990.4
Interquartile range (IQR)162132.55

Descriptive statistics

Standard deviation345292.07
Coefficient of variation (CV)2.1902368
Kurtosis54.15243
Mean157650.57
Median Absolute Deviation (MAD)29920.465
Skewness5.8370202
Sum1.6275845 × 109
Variance1.1922661 × 1011
MonotonicityNot monotonic
2024-08-28T14:58:51.904483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 29
 
0.3%
16000 23
 
0.2%
800 18
 
0.2%
0 17
 
0.2%
14400 16
 
0.2%
3200 15
 
0.1%
244216 15
 
0.1%
120000 13
 
0.1%
160 11
 
0.1%
250 11
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.12 1
 
< 0.1%
0.2 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.42 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5951990.4 1
< 0.1%
5768697.6 1
< 0.1%
5329891.2 1
< 0.1%
5140114.74 1
< 0.1%
4959241.98 1
< 0.1%
4278871.84 1
< 0.1%
4228629.72 1
< 0.1%
4014000 1
< 0.1%
3932880 1
< 0.1%
3904000 2
< 0.1%

unit_price
Real number (ℝ)

SKEWED 

Distinct183
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61170089
Minimum0
Maximum238.65
Zeros103
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:52.198539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.08
median0.16
Q30.47
95-th percentile1.6
Maximum238.65
Range238.65
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation3.2758077
Coefficient of variation (CV)5.3552443
Kurtosis2725.9603
Mean0.61170089
Median Absolute Deviation (MAD)0.12
Skewness40.584849
Sum6315.2
Variance10.730916
MonotonicityNot monotonic
2024-08-28T14:58:52.522499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 713
 
6.9%
0.01 492
 
4.8%
0.12 464
 
4.5%
0.14 444
 
4.3%
0.8 411
 
4.0%
0.11 400
 
3.9%
1.6 368
 
3.6%
0.05 343
 
3.3%
0.16 343
 
3.3%
0.19 321
 
3.1%
Other values (173) 6025
58.4%
ValueCountFrequency (%)
0 103
 
1.0%
0.01 492
4.8%
0.02 140
 
1.4%
0.03 250
 
2.4%
0.04 713
6.9%
0.05 343
3.3%
0.06 274
 
2.7%
0.07 248
 
2.4%
0.08 146
 
1.4%
0.09 154
 
1.5%
ValueCountFrequency (%)
238.65 1
 
< 0.1%
41.68 1
 
< 0.1%
37.5 2
 
< 0.1%
30 1
 
< 0.1%
26.91 1
 
< 0.1%
25 4
 
< 0.1%
24.85 3
 
< 0.1%
24.5 46
0.4%
23 23
0.2%
17.12 3
 
< 0.1%

weight_kilograms
Real number (ℝ)

MISSING  SKEWED 

Distinct3388
Distinct (%)53.2%
Missing3952
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean3424.4413
Minimum0
Maximum857354
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:52.818269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q1206.75
median1047
Q33334
95-th percentile12958.05
Maximum857354
Range857354
Interquartile range (IQR)3127.25

Descriptive statistics

Standard deviation13526.968
Coefficient of variation (CV)3.9501241
Kurtosis2538.1741
Mean3424.4413
Median Absolute Deviation (MAD)975
Skewness42.505903
Sum21820540
Variance1.8297887 × 108
MonotonicityNot monotonic
2024-08-28T14:58:53.101256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 29
 
0.3%
6 26
 
0.3%
1 23
 
0.2%
5 20
 
0.2%
60 20
 
0.2%
4 19
 
0.2%
3 18
 
0.2%
36 17
 
0.2%
12 17
 
0.2%
21 17
 
0.2%
Other values (3378) 6166
59.7%
(Missing) 3952
38.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 23
0.2%
2 29
0.3%
3 18
0.2%
4 19
0.2%
5 20
0.2%
6 26
0.3%
7 16
0.2%
8 11
 
0.1%
9 17
0.2%
ValueCountFrequency (%)
857354 1
< 0.1%
291096 1
< 0.1%
205503 1
< 0.1%
154780 1
< 0.1%
112027 1
< 0.1%
90446 1
< 0.1%
88761 1
< 0.1%
88190 1
< 0.1%
87076 1
< 0.1%
85128 1
< 0.1%

freight_cost_usd
Real number (ℝ)

MISSING 

Distinct5432
Distinct (%)87.6%
Missing4126
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean11103.235
Minimum0.75
Maximum289653.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:53.465834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile641.057
Q12131.12
median5869.655
Q314406.57
95-th percentile36679.879
Maximum289653.2
Range289652.45
Interquartile range (IQR)12275.45

Descriptive statistics

Standard deviation15813.027
Coefficient of variation (CV)1.424182
Kurtosis41.054884
Mean11103.235
Median Absolute Deviation (MAD)4493.845
Skewness4.6880226
Sum68817849
Variance2.5005181 × 108
MonotonicityNot monotonic
2024-08-28T14:58:53.925766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9736.1 36
 
0.3%
6147.18 27
 
0.3%
7445.8 16
 
0.2%
13398.06 16
 
0.2%
9341.49 15
 
0.1%
7329.83 12
 
0.1%
1211.48 11
 
0.1%
25231.96 11
 
0.1%
15322.73 10
 
0.1%
15459.09 10
 
0.1%
Other values (5422) 6034
58.4%
(Missing) 4126
40.0%
ValueCountFrequency (%)
0.75 1
< 0.1%
14.36 1
< 0.1%
17.72 1
< 0.1%
22.29 1
< 0.1%
29.21 1
< 0.1%
30 1
< 0.1%
30.49 1
< 0.1%
41 1
< 0.1%
42.35 1
< 0.1%
48 1
< 0.1%
ValueCountFrequency (%)
289653.2 1
< 0.1%
241407.27 1
< 0.1%
194623.44 1
< 0.1%
161962.32 1
< 0.1%
161712.87 1
< 0.1%
152368.7 1
< 0.1%
146850.66 1
< 0.1%
146734.85 1
< 0.1%
139951.34 1
< 0.1%
132890.27 1
< 0.1%

line_item_insurance_usd
Real number (ℝ)

MISSING 

Distinct6722
Distinct (%)67.0%
Missing287
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean240.11763
Minimum0
Maximum7708.44
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:54.468082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q16.51
median47.04
Q3252.4
95-th percentile1082.032
Maximum7708.44
Range7708.44
Interquartile range (IQR)245.89

Descriptive statistics

Standard deviation500.19057
Coefficient of variation (CV)2.0831064
Kurtosis34.911215
Mean240.11763
Median Absolute Deviation (MAD)46.27
Skewness4.8271624
Sum2410060.6
Variance250190.6
MonotonicityNot monotonic
2024-08-28T14:58:54.985906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
0.5%
0.02 37
 
0.4%
0.07 33
 
0.3%
0.05 30
 
0.3%
0.06 30
 
0.3%
0.01 26
 
0.3%
0.03 23
 
0.2%
0.09 21
 
0.2%
0.08 20
 
0.2%
0.49 18
 
0.2%
Other values (6712) 9745
94.4%
(Missing) 287
 
2.8%
ValueCountFrequency (%)
0 54
0.5%
0.01 26
0.3%
0.02 37
0.4%
0.03 23
0.2%
0.04 14
 
0.1%
0.05 30
0.3%
0.06 30
0.3%
0.07 33
0.3%
0.08 20
 
0.2%
0.09 21
 
0.2%
ValueCountFrequency (%)
7708.44 1
< 0.1%
7005.49 1
< 0.1%
5930.22 1
< 0.1%
5573.31 1
< 0.1%
5479.13 1
< 0.1%
5284.04 1
< 0.1%
5230.81 1
< 0.1%
5162.29 1
< 0.1%
5145 2
< 0.1%
5098.1 1
< 0.1%

item_insurance_rp_mean
Real number (ℝ)

Distinct6723
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.11763
Minimum0
Maximum7708.44
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:55.426871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26
Q17.03
median52.94
Q3241.75
95-th percentile1061.35
Maximum7708.44
Range7708.44
Interquartile range (IQR)234.72

Descriptive statistics

Standard deviation493.18841
Coefficient of variation (CV)2.053945
Kurtosis35.994766
Mean240.11763
Median Absolute Deviation (MAD)52.12
Skewness4.89567
Sum2478974.4
Variance243234.81
MonotonicityNot monotonic
2024-08-28T14:58:55.916247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240.1176258 287
 
2.8%
0 54
 
0.5%
0.02 37
 
0.4%
0.07 33
 
0.3%
0.05 30
 
0.3%
0.06 30
 
0.3%
0.01 26
 
0.3%
0.03 23
 
0.2%
0.09 21
 
0.2%
0.08 20
 
0.2%
Other values (6713) 9763
94.6%
ValueCountFrequency (%)
0 54
0.5%
0.01 26
0.3%
0.02 37
0.4%
0.03 23
0.2%
0.04 14
 
0.1%
0.05 30
0.3%
0.06 30
0.3%
0.07 33
0.3%
0.08 20
 
0.2%
0.09 21
 
0.2%
ValueCountFrequency (%)
7708.44 1
< 0.1%
7005.49 1
< 0.1%
5930.22 1
< 0.1%
5573.31 1
< 0.1%
5479.13 1
< 0.1%
5284.04 1
< 0.1%
5230.81 1
< 0.1%
5162.29 1
< 0.1%
5145 2
< 0.1%
5098.1 1
< 0.1%

item_insurance_rp_kNN
Real number (ℝ)

Distinct6916
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.23942
Minimum0
Maximum7708.44
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:56.419365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26
Q16.6475
median47.05
Q3250.0675
95-th percentile1067.45
Maximum7708.44
Range7708.44
Interquartile range (IQR)243.42

Descriptive statistics

Standard deviation494.929
Coefficient of variation (CV)2.0862005
Kurtosis35.571588
Mean237.23942
Median Absolute Deviation (MAD)46.23
Skewness4.8652938
Sum2449259.8
Variance244954.71
MonotonicityNot monotonic
2024-08-28T14:58:56.860730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
0.5%
0.02 37
 
0.4%
0.07 33
 
0.3%
0.05 30
 
0.3%
0.06 30
 
0.3%
0.01 26
 
0.3%
0.03 23
 
0.2%
0.09 21
 
0.2%
0.08 20
 
0.2%
0.12 18
 
0.2%
Other values (6906) 10032
97.2%
ValueCountFrequency (%)
0 54
0.5%
0.01 26
0.3%
0.02 37
0.4%
0.03 23
0.2%
0.04 14
 
0.1%
0.05 30
0.3%
0.06 30
0.3%
0.07 33
0.3%
0.08 20
 
0.2%
0.09 21
 
0.2%
ValueCountFrequency (%)
7708.44 1
< 0.1%
7005.49 1
< 0.1%
5930.22 1
< 0.1%
5573.31 1
< 0.1%
5479.13 1
< 0.1%
5284.04 1
< 0.1%
5230.81 1
< 0.1%
5162.29 1
< 0.1%
5145 2
< 0.1%
5098.1 1
< 0.1%

line_item_quantity_norm
Real number (ℝ)

Distinct5065
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029567087
Minimum0
Maximum1
Zeros35
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:57.166004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5806535 × 10-5
Q10.00065645373
median0.0048371124
Q30.027481943
95-th percentile0.14669491
Maximum1
Range1
Interquartile range (IQR)0.02682549

Descriptive statistics

Standard deviation0.064573278
Coefficient of variation (CV)2.183958
Kurtosis40.0503
Mean0.029567087
Median Absolute Deviation (MAD)0.0047580799
Skewness5.0383147
Sum305.25061
Variance0.0041697082
MonotonicityNot monotonic
2024-08-28T14:58:57.471769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01612747138 93
 
0.9%
0.00161129552 91
 
0.9%
0.0001596779344 87
 
0.8%
0.003224203949 73
 
0.7%
0.008062929235 69
 
0.7%
0.0008048413059 67
 
0.6%
0.03225655567 67
 
0.6%
0.004837112378 66
 
0.6%
3.225816857 × 10-663
 
0.6%
0.08064380853 62
 
0.6%
Other values (5055) 9586
92.9%
ValueCountFrequency (%)
0 35
0.3%
1.612908429 × 10-640
0.4%
3.225816857 × 10-663
0.6%
4.838725286 × 10-646
0.4%
6.451633715 × 10-628
0.3%
8.064542144 × 10-648
0.5%
9.677450572 × 10-627
0.3%
1.1290359 × 10-526
0.3%
1.290326743 × 10-522
 
0.2%
1.451617586 × 10-554
0.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9692047394 1
 
< 0.1%
0.895480308 1
 
< 0.1%
0.8306462279 3
< 0.1%
0.8298817093 1
 
< 0.1%
0.7420023936 1
 
< 0.1%
0.7096780957 1
 
< 0.1%
0.7071119584 1
 
< 0.1%
0.648324672 1
 
< 0.1%
0.6451617586 2
< 0.1%

line_item_quantity_z
Real number (ℝ)

Distinct5065
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4047593 × 10-17
Minimum-0.45788425
Maximum15.028398
Zeros0
Zeros (%)0.0%
Negative7833
Negative (%)75.9%
Memory size80.8 KiB
2024-08-28T14:58:57.784359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.45788425
5-th percentile-0.45748461
Q1-0.44771823
median-0.38297537
Q3-0.032291122
95-th percentile1.8138745
Maximum15.028398
Range15.486282
Interquartile range (IQR)0.4154271

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)2.2702716 × 1016
Kurtosis40.0503
Mean4.4047593 × 10-17
Median Absolute Deviation (MAD)0.073684968
Skewness5.0383147
Sum9.6750385 × 10-13
Variance1
MonotonicityNot monotonic
2024-08-28T14:58:58.076640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2081296819 93
 
0.9%
-0.4329312778 91
 
0.9%
-0.4554114374 87
 
0.8%
-0.4079533227 73
 
0.7%
-0.3330194574 69
 
0.7%
-0.4454202554 67
 
0.6%
0.04164986915 67
 
0.6%
-0.3829753676 66
 
0.6%
-0.4578342991 63
 
0.6%
0.7909885223 62
 
0.6%
Other values (5055) 9586
92.9%
ValueCountFrequency (%)
-0.457884255 35
0.3%
-0.457859277 40
0.4%
-0.4578342991 63
0.6%
-0.4578093211 46
0.4%
-0.4577843432 28
0.3%
-0.4577593652 48
0.5%
-0.4577343873 27
0.3%
-0.4577094093 26
0.3%
-0.4576844314 22
 
0.2%
-0.4576594534 54
0.5%
ValueCountFrequency (%)
15.02839795 1
 
< 0.1%
14.55149386 1
 
< 0.1%
13.40977651 1
 
< 0.1%
12.40573765 3
< 0.1%
12.3938981 1
 
< 0.1%
11.03297421 1
 
< 0.1%
10.53239101 1
 
< 0.1%
10.49265109 1
 
< 0.1%
9.582254579 1
 
< 0.1%
9.533272809 2
< 0.1%

line_item_value_norm
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026487033
Minimum0
Maximum1
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:58.388682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2354807 × 10-5
Q10.0007248991
median0.005119542
Q30.027964954
95-th percentile0.11808336
Maximum1
Range1
Interquartile range (IQR)0.027240055

Descriptive statistics

Standard deviation0.058012874
Coefficient of variation (CV)2.1902368
Kurtosis54.15243
Mean0.026487033
Median Absolute Deviation (MAD)0.005026968
Skewness5.8370202
Sum273.45213
Variance0.0033654935
MonotonicityNot monotonic
2024-08-28T14:58:58.700823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03360220473 29
 
0.3%
0.002688176379 23
 
0.2%
0.0001344088189 18
 
0.2%
0 17
 
0.2%
0.002419358741 16
 
0.2%
0.0005376352758 15
 
0.1%
0.04103098016 15
 
0.1%
0.02016132284 13
 
0.1%
2.688176379 × 10-511
 
0.1%
4.200275592 × 10-511
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
1.680110237 × 10-91
 
< 0.1%
5.04033071 × 10-91
 
< 0.1%
2.016132284 × 10-81
 
< 0.1%
3.360220473 × 10-81
 
< 0.1%
4.032264568 × 10-81
 
< 0.1%
4.200275592 × 10-81
 
< 0.1%
7.056462994 × 10-81
 
< 0.1%
8.400551184 × 10-81
 
< 0.1%
1.176077166 × 10-71
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.969204789 1
< 0.1%
0.8954804766 1
< 0.1%
0.8635959393 1
< 0.1%
0.8332073217 1
< 0.1%
0.718897638 1
< 0.1%
0.710456408 1
< 0.1%
0.674396249 1
< 0.1%
0.6607671948 1
< 0.1%
0.6559150364 2
< 0.1%

line_item_value_z
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2347451 × 10-17
Minimum-0.45657165
Maximum16.780982
Zeros0
Zeros (%)0.0%
Negative7651
Negative (%)74.1%
Memory size80.8 KiB
2024-08-28T14:58:59.012433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.45657165
5-th percentile-0.45601393
Q1-0.44407616
median-0.36832327
Q30.025475745
95-th percentile1.5788965
Maximum16.780982
Range17.237553
Interquartile range (IQR)0.4695519

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)3.0914337 × 1016
Kurtosis54.15243
Mean3.2347451 × 10-17
Median Absolute Deviation (MAD)0.086652628
Skewness5.8370202
Sum1.8771096 × 10-13
Variance1
MonotonicityNot monotonic
2024-08-28T14:58:59.311811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1226481483 29
 
0.3%
-0.4102340624 23
 
0.2%
-0.4542547668 18
 
0.2%
-0.4565716459 17
 
0.2%
-0.4148678207 16
 
0.2%
-0.4473041292 15
 
0.1%
0.2507020605 15
 
0.1%
-0.1090397694 13
 
0.1%
-0.4561082701 11
 
0.1%
-0.4558476212 11
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
-0.4565716459 17
0.2%
-0.456571617 1
 
< 0.1%
-0.456571559 1
 
< 0.1%
-0.4565712984 1
 
< 0.1%
-0.4565710667 1
 
< 0.1%
-0.4565709509 1
 
< 0.1%
-0.4565709219 1
 
< 0.1%
-0.4565704296 1
 
< 0.1%
-0.4565701979 1
 
< 0.1%
-0.4565696187 1
 
< 0.1%
ValueCountFrequency (%)
16.78098163 1
< 0.1%
16.25014754 1
< 0.1%
14.97932078 1
< 0.1%
14.42970937 1
< 0.1%
13.90588395 1
< 0.1%
11.93546469 1
< 0.1%
11.78995854 1
< 0.1%
11.16836963 1
< 0.1%
10.93343808 1
< 0.1%
10.84979874 2
< 0.1%

line_item_value_norm2
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026487033
Minimum0
Maximum1
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:58:59.632271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2354807 × 10-5
Q10.0007248991
median0.005119542
Q30.027964954
95-th percentile0.11808336
Maximum1
Range1
Interquartile range (IQR)0.027240055

Descriptive statistics

Standard deviation0.058012874
Coefficient of variation (CV)2.1902368
Kurtosis54.15243
Mean0.026487033
Median Absolute Deviation (MAD)0.005026968
Skewness5.8370202
Sum273.45213
Variance0.0033654935
MonotonicityNot monotonic
2024-08-28T14:58:59.940585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03360220473 29
 
0.3%
0.002688176379 23
 
0.2%
0.0001344088189 18
 
0.2%
0 17
 
0.2%
0.002419358741 16
 
0.2%
0.0005376352758 15
 
0.1%
0.04103098016 15
 
0.1%
0.02016132284 13
 
0.1%
2.688176379 × 10-511
 
0.1%
4.200275592 × 10-511
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
1.680110237 × 10-91
 
< 0.1%
5.04033071 × 10-91
 
< 0.1%
2.016132284 × 10-81
 
< 0.1%
3.360220473 × 10-81
 
< 0.1%
4.032264568 × 10-81
 
< 0.1%
4.200275592 × 10-81
 
< 0.1%
7.056462994 × 10-81
 
< 0.1%
8.400551184 × 10-81
 
< 0.1%
1.176077166 × 10-71
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.969204789 1
< 0.1%
0.8954804766 1
< 0.1%
0.8635959393 1
< 0.1%
0.8332073217 1
< 0.1%
0.718897638 1
< 0.1%
0.710456408 1
< 0.1%
0.674396249 1
< 0.1%
0.6607671948 1
< 0.1%
0.6559150364 2
< 0.1%

line_item_value_z2
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026487033
Minimum0
Maximum1
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:59:00.244414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2354807 × 10-5
Q10.0007248991
median0.005119542
Q30.027964954
95-th percentile0.11808336
Maximum1
Range1
Interquartile range (IQR)0.027240055

Descriptive statistics

Standard deviation0.058012874
Coefficient of variation (CV)2.1902368
Kurtosis54.15243
Mean0.026487033
Median Absolute Deviation (MAD)0.005026968
Skewness5.8370202
Sum273.45213
Variance0.0033654935
MonotonicityNot monotonic
2024-08-28T14:59:00.555440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03360220473 29
 
0.3%
0.002688176379 23
 
0.2%
0.0001344088189 18
 
0.2%
0 17
 
0.2%
0.002419358741 16
 
0.2%
0.0005376352758 15
 
0.1%
0.04103098016 15
 
0.1%
0.02016132284 13
 
0.1%
2.688176379 × 10-511
 
0.1%
4.200275592 × 10-511
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
1.680110237 × 10-91
 
< 0.1%
5.04033071 × 10-91
 
< 0.1%
2.016132284 × 10-81
 
< 0.1%
3.360220473 × 10-81
 
< 0.1%
4.032264568 × 10-81
 
< 0.1%
4.200275592 × 10-81
 
< 0.1%
7.056462994 × 10-81
 
< 0.1%
8.400551184 × 10-81
 
< 0.1%
1.176077166 × 10-71
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.969204789 1
< 0.1%
0.8954804766 1
< 0.1%
0.8635959393 1
< 0.1%
0.8332073217 1
< 0.1%
0.718897638 1
< 0.1%
0.710456408 1
< 0.1%
0.674396249 1
< 0.1%
0.6607671948 1
< 0.1%
0.6559150364 2
< 0.1%

line_item_value_log
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9890262
Minimum0
Maximum15.599236
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:59:00.859404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2656644
Q18.3699899
median10.324579
Q312.022439
95-th percentile13.462873
Maximum15.599236
Range15.599236
Interquartile range (IQR)3.6524491

Descriptive statistics

Standard deviation2.5886848
Coefficient of variation (CV)0.25915287
Kurtosis0.37076739
Mean9.9890262
Median Absolute Deviation (MAD)1.7945471
Skewness-0.71810505
Sum103126.71
Variance6.7012891
MonotonicityNot monotonic
2024-08-28T14:59:01.163453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.20607765 29
 
0.3%
9.680406499 23
 
0.2%
6.685860947 18
 
0.2%
0 17
 
0.2%
9.575052928 16
 
0.2%
8.07121854 15
 
0.1%
12.40581245 15
 
0.1%
11.69525536 13
 
0.1%
5.081404365 11
 
0.1%
5.525452939 11
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
0.009950330853 1
 
< 0.1%
0.02955880224 1
 
< 0.1%
0.1133286853 1
 
< 0.1%
0.1823215568 1
 
< 0.1%
0.2151113796 1
 
< 0.1%
0.2231435513 1
 
< 0.1%
0.3506568716 1
 
< 0.1%
0.4054651081 1
 
< 0.1%
0.5306282511 1
 
< 0.1%
ValueCountFrequency (%)
15.59923641 1
< 0.1%
15.56795707 1
< 0.1%
15.48884157 1
< 0.1%
15.45258615 1
< 0.1%
15.41676366 1
< 0.1%
15.26920018 1
< 0.1%
15.25738879 1
< 0.1%
15.20529906 1
< 0.1%
15.18488279 1
< 0.1%
15.17751248 2
< 0.1%

line_item_value_sqrt
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.90145
Minimum0
Maximum2439.6701
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:59:01.472637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.877146
Q165.685558
median174.56077
Q3407.97934
95-th percentile838.35015
Maximum2439.6701
Range2439.6701
Interquartile range (IQR)342.29378

Descriptive statistics

Standard deviation285.54659
Coefficient of variation (CV)1.0349587
Kurtosis4.9956214
Mean275.90145
Median Absolute Deviation (MAD)134.55358
Skewness1.865156
Sum2848406.5
Variance81536.857
MonotonicityNot monotonic
2024-08-28T14:59:01.767380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447.2135955 29
 
0.3%
126.4911064 23
 
0.2%
28.28427125 18
 
0.2%
0 17
 
0.2%
120 16
 
0.2%
56.56854249 15
 
0.1%
494.1821527 15
 
0.1%
346.4101615 13
 
0.1%
12.64911064 11
 
0.1%
15.8113883 11
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
0.1 1
 
< 0.1%
0.1732050808 1
 
< 0.1%
0.3464101615 1
 
< 0.1%
0.4472135955 1
 
< 0.1%
0.4898979486 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6480740698 1
 
< 0.1%
0.7071067812 1
 
< 0.1%
0.8366600265 1
 
< 0.1%
ValueCountFrequency (%)
2439.670142 1
< 0.1%
2401.811316 1
< 0.1%
2308.655713 1
< 0.1%
2267.182114 1
< 0.1%
2226.935558 1
< 0.1%
2068.54341 1
< 0.1%
2056.363227 1
< 0.1%
2003.496943 1
< 0.1%
1983.149011 1
< 0.1%
1975.854246 2
< 0.1%

line_item_value_boxcox
Real number (ℝ)

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.004539
Minimum0
Maximum40.709583
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-08-28T14:59:02.080202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.0948619
Q113.613772
median18.994234
Q324.685762
95-th percentile30.404996
Maximum40.709583
Range40.709583
Interquartile range (IQR)11.07199

Descriptive statistics

Standard deviation7.266032
Coefficient of variation (CV)0.3823314
Kurtosis-0.59898966
Mean19.004539
Median Absolute Deviation (MAD)5.5428869
Skewness-0.058302657
Sum196202.86
Variance52.795221
MonotonicityNot monotonic
2024-08-28T14:59:02.375177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.36632241 29
 
0.3%
17.0939656 23
 
0.2%
9.80756167 18
 
0.2%
0 17
 
0.2%
16.79558012 16
 
0.2%
12.8869904 15
 
0.1%
26.12203989 15
 
0.1%
23.50621449 13
 
0.1%
6.772463709 11
 
0.1%
7.560462508 11
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
0.009955685763 1
 
< 0.1%
0.02960609099 1
 
< 0.1%
0.1140259166 1
 
< 0.1%
0.1841306298 1
 
< 0.1%
0.2176326646 1
 
< 0.1%
0.2258574278 1
 
< 0.1%
0.3573896216 1
 
< 0.1%
0.4144849425 1
 
< 0.1%
0.5461466946 1
 
< 0.1%
ValueCountFrequency (%)
40.70958305 1
< 0.1%
40.54089811 1
< 0.1%
40.11677763 1
< 0.1%
39.92362947 1
< 0.1%
39.73352993 1
< 0.1%
38.9581715 1
< 0.1%
38.8966428 1
< 0.1%
38.62622884 1
< 0.1%
38.52065647 1
< 0.1%
38.48260185 2
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
True
6113 
False
4211 
ValueCountFrequency (%)
True 6113
59.2%
False 4211
40.8%
2024-08-28T14:59:02.680086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

shipment_mode_Air Charter
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
False
9674 
True
 
650
ValueCountFrequency (%)
False 9674
93.7%
True 650
 
6.3%
2024-08-28T14:59:02.888841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

shipment_mode_Ocean
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
False
9953 
True
 
371
ValueCountFrequency (%)
False 9953
96.4%
True 371
 
3.6%
2024-08-28T14:59:03.120182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
False
7494 
True
2830 
ValueCountFrequency (%)
False 7494
72.6%
True 2830
 
27.4%
2024-08-28T14:59:03.324451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

shipment_mode_missing
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
False
9964 
True
 
360
ValueCountFrequency (%)
False 9964
96.5%
True 360
 
3.5%
2024-08-28T14:59:03.533196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

weight_category
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing3952
Missing (%)38.3%
Memory size10.4 KiB
super-heavy
3249 
medium
1297 
light
1072 
heavy
754 

Length

Max length11
Median length11
Mean length8.2628688
Min length5

Characters and Unicode

Total characters52651
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlight
2nd rowmedium
3rd rowmedium
4th rowsuper-heavy
5th rowsuper-heavy

Common Values

ValueCountFrequency (%)
super-heavy 3249
31.5%
medium 1297
 
12.6%
light 1072
 
10.4%
heavy 754
 
7.3%
(Missing) 3952
38.3%

Length

2024-08-28T14:59:03.736050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T14:59:04.032275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
super-heavy 3249
51.0%
medium 1297
 
20.4%
light 1072
 
16.8%
heavy 754
 
11.8%

Most occurring characters

ValueCountFrequency (%)
e 8549
16.2%
h 5075
9.6%
u 4546
8.6%
a 4003
7.6%
v 4003
7.6%
y 4003
7.6%
s 3249
 
6.2%
p 3249
 
6.2%
r 3249
 
6.2%
- 3249
 
6.2%
Other values (6) 9476
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8549
16.2%
h 5075
9.6%
u 4546
8.6%
a 4003
7.6%
v 4003
7.6%
y 4003
7.6%
s 3249
 
6.2%
p 3249
 
6.2%
r 3249
 
6.2%
- 3249
 
6.2%
Other values (6) 9476
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8549
16.2%
h 5075
9.6%
u 4546
8.6%
a 4003
7.6%
v 4003
7.6%
y 4003
7.6%
s 3249
 
6.2%
p 3249
 
6.2%
r 3249
 
6.2%
- 3249
 
6.2%
Other values (6) 9476
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8549
16.2%
h 5075
9.6%
u 4546
8.6%
a 4003
7.6%
v 4003
7.6%
y 4003
7.6%
s 3249
 
6.2%
p 3249
 
6.2%
r 3249
 
6.2%
- 3249
 
6.2%
Other values (6) 9476
18.0%

Interactions

2024-08-28T14:58:35.168580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:55.940939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:05.506742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:11.240306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:17.513831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:22.745138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:27.382517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:34.943748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:39.756109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:45.257901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:51.221671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:55.883524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:01.792781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:06.463603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:11.256763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:18.439958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:24.146940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:30.443503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:35.424013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:56.624058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:05.790765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:11.533623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:17.890660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:22.984571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:27.638985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:35.215255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:40.003100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:45.577109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:51.461825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:56.136899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:02.025128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:06.702873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:11.580863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:18.679076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:24.432521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:30.698582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:35.691272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:57.437653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:06.120018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:11.862182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:18.288615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:23.242485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:27.914869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:35.477077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:40.260809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:45.987509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:51.723898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:56.409574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:02.299047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:06.956765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:11.948187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:18.998292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:24.719412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:30.951295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:35.969580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:58.033977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:06.412313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:12.155057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:18.714309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:23.499343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:30.090832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:35.736912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:40.559015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:46.431221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:51.990907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:56.671098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:02.580293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:07.215674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:12.318500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:19.308758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:24.979413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:31.201684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:36.219028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:58.392887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:06.718940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:12.453640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:19.084459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:23.758642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:30.463760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:35.984469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:40.808196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:46.771816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:52.227757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:56.912636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:02.825336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:07.483855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:12.639429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:19.571888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:25.365433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:31.448471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:36.472567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:59.137510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:07.038103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:12.760656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:19.330397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:23.982509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:30.849036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:36.266230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:41.061545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:47.130561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:52.478655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:57.179685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:03.066694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:07.742275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:12.993677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:19.830891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:25.730709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:31.694202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:36.736876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:56:59.961029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:07.374491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:13.091600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:19.591702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:24.228453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:31.219702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:36.549906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:41.340333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:47.411891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:52.738610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:57.582852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:03.332994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:07.996937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:13.351864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:20.114929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:26.134013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:31.950978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:37.027208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:00.563519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:07.712038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:13.393441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:19.853277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:24.517289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:31.588583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:36.820411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:41.612927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:47.702341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:53.010400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:57.972499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:03.604241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:08.266163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:13.733095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:20.563182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:26.557038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:32.220593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:37.301781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:01.231210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:08.054306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:13.694230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:20.119671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:24.800335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:31.969724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:37.089746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:41.884128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:47.976638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:53.271884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:58.371856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:03.867984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:08.565279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:14.164529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:20.982051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:26.985080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:32.488030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:37.602180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:01.933323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:08.402755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:14.012878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:20.374795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:25.059466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:32.401969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:37.368320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:42.150588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:48.249253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:53.558595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:58.677588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:04.125958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:08.838058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:14.525869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:21.392985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:27.388424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:32.770777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:37.863675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:02.320610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:08.710810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:14.347781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:20.638589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:25.304577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:32.722942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:37.626147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:42.420087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:48.513316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:53.802880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:59.037185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:04.373653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:09.090395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:15.318919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:21.789129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:27.782803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:33.012396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:38.143214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:02.719038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:09.063502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:14.642656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:20.901521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:25.576358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:33.122589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:37.889594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:42.689329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:48.792353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:54.074745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:59.473996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:04.637839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:09.348768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:15.634269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:22.132715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:28.160386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:33.268988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:38.399878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:03.131750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:09.356094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:15.005909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:21.141783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:25.830201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:33.368770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:38.149000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:42.947266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:49.069073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:54.314969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:59.817457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:04.878190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:09.626480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:15.901787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:22.486489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:28.575773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:33.536919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:38.666328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:03.549550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:09.655672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:15.481203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:21.416029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:26.090022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:33.640347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:38.430081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:43.291994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:49.347982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:54.584744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:00.205742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:05.141213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:09.884485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:16.163810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:22.842405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:29.004813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:33.824381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:38.925431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:03.953212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:09.954308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:15.848059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:21.684931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:26.351248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:33.886182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:38.688254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:43.664663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:49.640433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:54.827651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:00.548674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:05.397254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:10.140712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:16.412462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:23.092170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:29.344064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:34.070032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:39.184197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:04.308549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:10.278041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:16.345492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:21.932957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:26.603691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:34.156774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:38.944396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:44.062791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:49.908650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:55.091583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:00.898381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:05.665382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:10.408087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:17.574799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:23.346962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:29.628353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:34.336243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:39.551321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:04.830180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:10.584794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:16.725805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:22.199713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:26.875780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:34.430064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:39.222096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:44.511196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:50.177819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:55.358020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:01.256994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:05.935143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:10.697895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:17.836815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:23.622319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:29.896193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:34.636598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:39.944894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:05.147757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:10.883064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:17.078123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:22.468933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:27.122012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:34.679891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:39.488494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:44.850232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:50.449858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:57:55.620378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:01.517596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:06.174761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:10.949355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:18.138272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:23.864798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:30.147930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-28T14:58:34.904145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-08-28T14:58:40.674632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-28T14:58:42.116235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-28T14:58:43.032613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idcountryshipment_modescheduled_delivery_datesub_classificationvendoritem_iditem_descriptionbrandline_item_quantityline_item_valueunit_priceweight_kilogramsfreight_cost_usdline_item_insurance_usditem_insurance_rp_meanitem_insurance_rp_kNNline_item_quantity_normline_item_quantity_zline_item_value_normline_item_value_zline_item_value_norm2line_item_value_z2line_item_value_logline_item_value_sqrtline_item_value_boxcoxshipment_mode_Airshipment_mode_Air Chartershipment_mode_Oceanshipment_mode_Truckshipment_mode_missingweight_category
01Côte d'IvoireAir2006-06-02HIV testRANBAXY Fine Chemicals LTD.SKU0000HIV, Reveal G3 Rapid HIV-1 Antibody Test, 30 TestsReveal19551.000.9713.0780.34NaN240.1176260.7250.000029-0.4574350.000093-0.4549760.0000930.0000936.31354823.4733899.055646TrueFalseFalseFalseFalselight
13VietnamAir2006-11-14PediatricAurobindo Pharma LimitedSKU0001Nevirapine 10mg/ml, oral suspension, Bottle, 240 mlGeneric10006200.000.03358.04521.50NaN240.1176268.9550.001611-0.4329310.001042-0.4386160.0010420.0010428.73246678.74007914.527632TrueFalseFalseFalseFalsemedium
24Côte d'IvoireAir2006-08-27HIV testAbbott GmbH & Co. KGSKU0002HIV 1/2, Determine Complete HIV Kit, 100 TestsDetermine50040000.000.80171.01653.78NaN240.11762671.2000.000805-0.4454200.006720-0.3407280.0067200.00672010.596660200.00000019.837478TrueFalseFalseFalseFalsemedium
315VietnamAir2006-09-01AdultSUN PHARMACEUTICAL INDUSTRIES LTD (RANBAXY LABORATORIES LIMITED)SKU0003Lamivudine 150mg, tablets, 60 TabsGeneric31920127360.800.071855.016007.06NaN240.117626194.2850.0514820.3393870.021398-0.0877220.0213980.02139811.754787356.87644923.717742TrueFalseFalseFalseFalsesuper-heavy
416VietnamAir2006-08-11AdultAurobindo Pharma LimitedSKU0004Stavudine 30mg, capsules, 60 CapsGeneric38000121600.000.057590.045450.08NaN240.117626238.5700.0612890.4912530.020430-0.1044060.0204300.02043011.708500348.71191523.553159TrueFalseFalseFalseFalsesuper-heavy
523NigeriaAir2006-09-28PediatricAurobindo Pharma LimitedSKU0005Zidovudine 10mg/ml, oral solution, Bottle, 240 mlGeneric4162225.600.02504.05920.42NaN240.1176262.2800.000669-0.4475180.000374-0.4501260.0003740.0003747.70823147.17626512.035014TrueFalseFalseFalseFalseheavy
644ZambiaAir2007-01-08PediatricMERCK SHARP & DOHME IDEA GMBH (FORMALLY MERCK SHARP & DOHME B.V.)SKU0006Efavirenz 200mg [Stocrin/Sustiva], capsule, 90 CapsStocrin/Sustiva1354374.000.36328.0NaNNaN240.1176264.9600.000216-0.4545370.000735-0.4439040.0007350.0007358.38366266.13622313.647595TrueFalseFalseFalseFalsemedium
745TanzaniaAir2006-11-24AdultAurobindo Pharma LimitedSKU0007Nevirapine 200mg, tablets, 60 TabsGeneric1666760834.550.061478.06212.41NaN240.11762690.6150.026881-0.0416020.010221-0.2803890.0102210.01022111.015930246.64661021.186451TrueFalseFalseFalseFalsesuper-heavy
846NigeriaAir2006-12-07AdultAurobindo Pharma LimitedSKU0004Stavudine 30mg, capsules, 60 CapsGeneric273532.350.03NaNNaNNaN240.1176260.5500.000439-0.4510900.000089-0.4550300.0000890.0000896.27917823.0727118.987747TrueFalseFalseFalseFalseNaN
947ZambiaAir2007-01-30AdultABBVIE LOGISTICS (FORMERLY ABBOTT LOGISTICS BV)SKU0008Lopinavir/Ritonavir 200/50mg [Aluvia], tablets, 120 TabsAluvia2800115080.000.34643.0NaNNaN240.117626193.6150.004515-0.3879710.019335-0.1232890.0193350.01933511.653392339.23443223.358278TrueFalseFalseFalseFalseheavy
idcountryshipment_modescheduled_delivery_datesub_classificationvendoritem_iditem_descriptionbrandline_item_quantityline_item_valueunit_priceweight_kilogramsfreight_cost_usdline_item_insurance_usditem_insurance_rp_meanitem_insurance_rp_kNNline_item_quantity_normline_item_quantity_zline_item_value_normline_item_value_zline_item_value_norm2line_item_value_z2line_item_value_logline_item_value_sqrtline_item_value_boxcoxshipment_mode_Airshipment_mode_Air Chartershipment_mode_Oceanshipment_mode_Truckshipment_mode_missingweight_category
1031486813NigeriaAir Charter2015-06-30PediatricSCMS from RDCSKU0140Lamivudine/Nevirapine/Zidovudine 30/50/60mg, dispersible tablets, 60 TabsGeneric1034037224.000.06NaNNaN38.2738.2738.270.016676-0.1996370.006254-0.3487670.0062540.00625410.524736192.93522219.612151FalseTrueFalseFalseFalseNaN
1031586814NigeriaAir Charter2015-06-30AdultSCMS from RDCSKU0008Lopinavir/Ritonavir 200/50mg [Aluvia], tablets, 120 TabsAluvia700001304800.000.1615198.026180.01341.331341.331341.330.1129021.2905480.2192213.3222580.2192210.21922114.0815611142.27842533.148511FalseTrueFalseFalseFalsesuper-heavy
1031686815NigeriaAir Charter2015-06-02AdultSCMS from RDCSKU0011Lamivudine/Zidovudine 150/300mg, tablets, 60 TabsGeneric1500097800.000.111547.03410.0115.11115.11115.110.024192-0.0832400.016431-0.1733330.0164310.01643111.490690312.72991522.789648FalseTrueFalseFalseFalsesuper-heavy
1031786816NigeriaAir2015-06-30AdultSCMS from RDCSKU0026Efavirenz 600mg, tablets, 30 TabsGeneric672420978.880.10NaNNaN24.6924.6924.690.010844-0.2899570.003525-0.3958150.0035250.0035259.951319144.84087817.877045TrueFalseFalseFalseFalseNaN
1031886817ZimbabweTruck2015-07-31PediatricSCMS from RDCSKU0140Lamivudine/Nevirapine/Zidovudine 30/50/60mg, dispersible tablets, 60 TabsGeneric205243738874.800.06NaNNaN869.66869.66869.660.3310374.6686410.1241391.6832830.1241390.12413913.512885859.57826930.620015FalseFalseFalseTrueFalseNaN
1031986818ZimbabweTruck2015-07-31PediatricSCMS from RDCSKU0140Lamivudine/Nevirapine/Zidovudine 30/50/60mg, dispersible tablets, 60 TabsGeneric166571599655.600.06NaNNaN705.79705.79705.790.2686623.7026940.1007491.2800900.1007490.10074913.304112774.37432829.730083FalseFalseFalseTrueFalseNaN
1032086819Côte d'IvoireTruck2015-07-31AdultSCMS from RDCSKU0011Lamivudine/Zidovudine 150/300mg, tablets, 60 TabsGeneric21072137389.440.11NaNNaN161.71161.71161.710.0339860.0684260.023083-0.0586780.0230830.02308311.830582370.66081523.989033FalseFalseFalseTrueFalseNaN
1032186821ZambiaTruck2015-08-31AdultSCMS from RDCSKU0113Efavirenz/Lamivudine/Tenofovir Disoproxil Fumarate 600/300/300mg, tablets, 30 TabsGeneric5145265140114.740.33NaNNaN5284.045284.045284.040.82988212.3938980.86359614.4297090.8635960.86359615.4525862267.18211439.923629FalseFalseFalseTrueFalseNaN
1032286822ZimbabweTruck2015-09-09AdultSCMS from RDCSKU0011Lamivudine/Zidovudine 150/300mg, tablets, 60 TabsGeneric17465113871.800.111392.0NaN134.03134.03134.030.028168-0.0216690.019132-0.1267880.0191320.01913211.642837337.44895923.321088FalseFalseFalseTrueFalsesuper-heavy
1032386823ZimbabweTruck2015-08-31PediatricSCMS from RDCSKU0124Lamivudine/Zidovudine 30/60mg, dispersible tablets, 60 TabsGeneric3663972911.610.03NaNNaN85.8285.8285.820.0590940.4572580.012250-0.2454120.0122500.01225011.197017270.02149921.788268FalseFalseFalseTrueFalseNaN